title: Feature extraction of osteoporosis risk factors using artificial neural networks and genetic algorithms.
abstract: A hybrid model that consists of an Artificial Neural Network and Genetic Algorithm is used in order to select the most significant osteoporosis risk factors. The results indicated that just 8 to 10 parameters, out of a total 34, are essential in order of high performance to be achieved.
authors.affiliation: Medical Informatics Laboratory, Medical School, Democritus University of Thrace, GR-68100, GREECE. anasta@med.duth.gr, dgaliatsatos2010@hotmail.com.
authors.forename: George
authors.initials: G
authors.lastname: Anastassopoulos
authors.forename: Adam
authors.initials: A
authors.lastname: Adamopoulos
authors.forename: Dimitrios
authors.initials: D
authors.lastname: Galiatsatos
authors.forename: Georgios
authors.initials: G
authors.lastname: Drosos
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008957:Models, Genetic; D016571:Neural Networks, Computer; D010024:Osteoporosis / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011379:Prognosis; D018570:Risk Assessment / Q000379:methods